Process industries, which include mineral and oil refining, chemical and petrochemical production, fossil and nuclear power generation, parts of the food and pharmaceutical industries to name a few, are generally characterized by continuous fluid processing, with unit operations such as reactions, purification, and energy exchange. This stands in contrast with discrete parts manufacturing industries, such as electronics or automobile manufacture.
Process control systems generally consist of many direct control variables, such as flow controllers, temperature controllers, and pressure controllers, and also many indirect control variables, such as temperatures, pressures and flows that are not directly controlled, but which are affected by various process influences, including the direct controllers. The multiple interactions between the direct controllers (as well as other independent variables) and the indirect variables (or dependent variables) comprise the multivariable nature of most continuous processes.
Prior to the advent of computer-based process control systems, ca. 1980s, multivariable control, i.e. controlling direct and indirect control variables in a coordinated manner, was accomplished manually. It was part of the operator's job to adjust the direct controllers in order to keep them and the indirect variables within specified constraint limits, as well as to affect greater economic optimization, for example, to make incrementally more product, or higher quality product, or make more efficient use of raw materials and energy.
Due to the degree of unwieldiness of most industrial scale processes, their susceptibility to many sources of upsets, the often sharp safety and cost consequences of exceeding constraints, and in the absence of automatic multivariable constraint controls, process operation traditionally is kept well away from critical constraints. However, in most cases, operating away from constraints translates into additional operating expense, i.e. results in making less product or consuming more energy or raw materials. This is an essential aspect of operating most industrial scale continuous processes—keeping the process within a safe and reliable operating window, while pushing overall process economics.
With the advent of computer-based control systems and appropriate control methods in the 1980s, automated multivariable control became established as a viable and often important part of modern process control systems. With automated multivariable control, many processes are able to operate reliably closer to constraint limits and optimization targets, with potentially significant benefits in product quality, efficiency, throughput, reliability and safety.
In the process industries, the dominant method of multivariable control has been multivariable model-based predictive control (MPC). “Models” are mathematical descriptions of the process interactions between the direct control variables and the indirect control variables (or, between the independent and dependent variables). MPC uses models in its constraint control and optimization algorithms (along with cost and other factors). Since the 1980s, thousands of instances of MPC have been deployed in industry.
Its success aside, MPC has experienced several persistent shortcomings. The inventor has been a leading industry voice for understanding and improving MPC effectiveness, having published numerous trade journal articles on the topic, and has generally concluded that model-based control has a fundamental weakness in that models are inherently inaccurate (because actual process gains change dynamically), and moreover that the aggressive nature of model-based control is unnecessary and even undesirable from a process operation standpoint, where gradual constraint control and optimization is more prudent and assuring process stability is a high priority.
Multivariable control has been an active technology area in the process industries. However, to the inventor's knowledge all prior patents relate to methods of model-based control and/or optimization methods. The present invention is a method for multivariable control that is not model-based, and which is independent of any optimizer method employed. A search has revealed no other patents for a non-model-based approach to multivariable control.
In response, the inventor has developed the present invention, which inherently adapts to changes in process gain, yet is still predictive and accomplishes multivariable constraint control and optimization in a gradual manner that assures ongoing process stability, in addition to several other control and operating advantages that are described.